US5754972A - Speech recognition system for languages with compound words - Google Patents

Speech recognition system for languages with compound words Download PDF

Info

Publication number
US5754972A
US5754972A US08/437,965 US43796595A US5754972A US 5754972 A US5754972 A US 5754972A US 43796595 A US43796595 A US 43796595A US 5754972 A US5754972 A US 5754972A
Authority
US
United States
Prior art keywords
word
formatives
compound
words
mode
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Lifetime
Application number
US08/437,965
Other languages
English (en)
Inventor
James K. Baker
Paul G. Bamberg
Jed M. Roberts
Caroline B. Huang
Stijn VanEven
Claudia L. E. Ellermann
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nuance Communications Inc
Original Assignee
L&H Holdings USA Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by L&H Holdings USA Inc filed Critical L&H Holdings USA Inc
Priority to US08/437,965 priority Critical patent/US5754972A/en
Application granted granted Critical
Publication of US5754972A publication Critical patent/US5754972A/en
Assigned to SCANSOFT, INC. reassignment SCANSOFT, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: L & H HOLDINGS USA, INC.
Assigned to L & H HOLDINGS USA, INC. reassignment L & H HOLDINGS USA, INC. MERGER (SEE DOCUMENT FOR DETAILS). Assignors: DRAGON SYSTEMS, INC.
Assigned to NUANCE COMMUNICATIONS, INC. reassignment NUANCE COMMUNICATIONS, INC. CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: SCANSOFT, INC.
Assigned to USB AG, STAMFORD BRANCH reassignment USB AG, STAMFORD BRANCH SECURITY AGREEMENT Assignors: NUANCE COMMUNICATIONS, INC.
Assigned to USB AG. STAMFORD BRANCH reassignment USB AG. STAMFORD BRANCH SECURITY AGREEMENT Assignors: NUANCE COMMUNICATIONS, INC.
Anticipated expiration legal-status Critical
Assigned to MITSUBISH DENKI KABUSHIKI KAISHA, AS GRANTOR, NORTHROP GRUMMAN CORPORATION, A DELAWARE CORPORATION, AS GRANTOR, STRYKER LEIBINGER GMBH & CO., KG, AS GRANTOR, ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DELAWARE CORPORATION, AS GRANTOR, NUANCE COMMUNICATIONS, INC., AS GRANTOR, SCANSOFT, INC., A DELAWARE CORPORATION, AS GRANTOR, SPEECHWORKS INTERNATIONAL, INC., A DELAWARE CORPORATION, AS GRANTOR, DICTAPHONE CORPORATION, A DELAWARE CORPORATION, AS GRANTOR, HUMAN CAPITAL RESOURCES, INC., A DELAWARE CORPORATION, AS GRANTOR, TELELOGUE, INC., A DELAWARE CORPORATION, AS GRANTOR, DSP, INC., D/B/A DIAMOND EQUIPMENT, A MAINE CORPORATON, AS GRANTOR, NOKIA CORPORATION, AS GRANTOR, INSTITIT KATALIZA IMENI G.K. BORESKOVA SIBIRSKOGO OTDELENIA ROSSIISKOI AKADEMII NAUK, AS GRANTOR reassignment MITSUBISH DENKI KABUSHIKI KAISHA, AS GRANTOR PATENT RELEASE (REEL:018160/FRAME:0909) Assignors: MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT
Assigned to ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DELAWARE CORPORATION, AS GRANTOR, NUANCE COMMUNICATIONS, INC., AS GRANTOR, SCANSOFT, INC., A DELAWARE CORPORATION, AS GRANTOR, SPEECHWORKS INTERNATIONAL, INC., A DELAWARE CORPORATION, AS GRANTOR, DICTAPHONE CORPORATION, A DELAWARE CORPORATION, AS GRANTOR, TELELOGUE, INC., A DELAWARE CORPORATION, AS GRANTOR, DSP, INC., D/B/A DIAMOND EQUIPMENT, A MAINE CORPORATON, AS GRANTOR reassignment ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DELAWARE CORPORATION, AS GRANTOR PATENT RELEASE (REEL:017435/FRAME:0199) Assignors: MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT
Expired - Lifetime legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/04—Segmentation; Word boundary detection
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • G10L15/183—Speech classification or search using natural language modelling using context dependencies, e.g. language models
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L2015/085—Methods for reducing search complexity, pruning
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L2015/088—Word spotting

Definitions

  • This invention relates generally to speech recognition systems, and in particular, to methods and apparatus for recognizing compound words.
  • Many present speech recognition systems operate by matching a representation of an acoustic signal generated in response to an utterance of the word to be recognized against acoustic descriptions, or models of words in a vocabulary.
  • an analog to digital converter converts the acoustic signal, generated by speaking the word to be recognized, into a digital representation. That digital representation is subsequently transformed into a frequency domain signal.
  • the frequency domain signal consists of a sequence of frames, each giving the amplitude of the acoustic signal in one of a plurality of frequency bands over a brief interval of time.
  • Such systems commonly operate by comparing the sequence of frames, derived from the word to be recognized, with a sequence of nodes, or frame models, stored in the active vocabulary for the system.
  • the performance of early frame matching systems was relatively poor. The individual sounds which make up a given word are seldom, if ever, spoken at exactly the same rate or in exactly the same manner in any two utterances of that word. Consequently, two techniques developed for improving the performance of those systems.
  • the first is probabilistic matching.
  • probabilistic matching the system determines the likelihood that a particular frame of an utterance corresponds to a particular node in the acoustic model of a word.
  • the likelihood determination is a function of how closely the actual amplitude of each frequency band of the spoken frame matches the expected amplitude of the frequency bands stored as frame models.
  • Probabilistic matching also takes into account how the deviation between the actual and expected amplitudes in each frequency band compare to the expected deviation for such values.
  • Probabilistic matching causes speech recognition systems to have an improved ability to cope with variations in audio signals which occur with respect to different utterances of the same word. It also enables systems to deal more effectively with noise which is commonly present during speech recognition tasks.
  • Dynamic programming provides a method to find an optimal, or near optimal, match between the sequence of frames produced by an utterance and the sequence of nodes contained in the model of a word. It does this by expanding and contracting the duration of each node in the acoustic model of a word to compensate for natural variations which occur in different utterances of the same word.
  • the prior art has developed a variety of methods for dealing with the excessive computational demands introduced by large vocabulary recognition systems.
  • One method provides the system with an artificial grammar which limits the vocabulary that the system can recognize at any one time to a sub-set of the overall vocabulary.
  • pre-filtering Another prior art technique for making large vocabulary recognition more efficient is commonly referred to as "pre-filtering” or “pruning.” Both pre-filtering and pruning involve reducing the number of comparisons which a program considers, by eliminating from further consideration those acoustic models which do not appear to warrant further computation.
  • the dynamic programming algorithm produces a score for each word in its active vocabulary after each frame of an utterance is received. This score corresponds to the likelihood that the frames received so far match the given word. After the score for each word in the active vocabulary is updated, it is compared with the best score produced for any word. If the difference between the score for a given word and the best score exceeds a certain threshold, that given word is removed from the active vocabulary and future frames are no longer compared against it.
  • a language model predicts the relative likelihood of the occurrence of each word in the system's vocabulary, given the word spoken before it.
  • Language model probabilities are calculated by analyzing a large body of text and determining from it the number of times that each word in the vocabulary is preceded by each other word in the vocabulary.
  • the prior art systems upon recognizing a spoken word, typically display a choice list of the words most likely to be the spoken word. However, the amount of time required before some prior art systems display any words for the operator to chose increases with the size of the recognition vocabulary. In large vocabulary systems, the prior art systems are relatively slow. A later prior art system utilizes methods, such as alphabetic filtering, to improve system response time. Alphabetic filtering enables a user to narrow the field of search by specifying a character string contained in the spoken word. The features of that system are further described in U.S. Pat. No. 4,866,778.
  • Prior art systems achieve approximately a ninety-five percent coverage of the words in the American English language. It is considerably more difficult to achieve a comparable coverage in languages having a high proportion of words formed by combining components. According to prior art systems, to reach the desired level of coverage, the active vocabulary of the system would have to be expanded by creating and storing acoustic models for a significant number of compound words. Such an approach would involve an impractical amount of storage capacity. Additionally, the problem of designing computationally efficient speech recognition systems is made even more difficult by significantly increasing the number of acoustic models stored in the system library.
  • the present invention is a system and associated methods for recognizing compound words from an utterance containing a succession of one or more words from a predetermined vocabulary. At least one of the words in the utterance is a compound word including at least two formatives in succession, wherein those formatives are words in the vocabulary.
  • the system includes a word recognizer element.
  • the word recognizer element is selectively operative in a first mode for identifying words corresponding to the compound words, in response to processing the utterance spoken by the user.
  • the word recognizer is also selectively operative in a second mode for identifying words corresponding to the compound words, in response to processing a succession of formatives spoken by a user, wherein each of the formatives is representative of a correspondingly positioned formative in the compound word.
  • the word recognizer may include an element for recognizing the compound words from an utterance of words spoken in isolation or spoken as continuous speech.
  • the system also includes a user controlled element for determining the mode of the word recognizer.
  • the user controlled element may be responsive to a user generated auditory signal or to a user generated keyboard command to determine the mode of the word recognizer.
  • the system is operative in a sub-mode of the above mentioned second mode.
  • the word recognizer element identifies words corresponding to the compound words, in response to processing keywords, entered by a user.
  • Each of the keywords is representative of a correspondingly positioned character in the compound word.
  • the keywords may correspond to any alphabet type representations.
  • the keywords may correspond to International Communication Alphabet words. Alternatively, they may correspond to German Telephone Code words.
  • the user may be able to enter the keywords through user generated auditory signals or through user generated keyboard commands.
  • the functions encompassed by this sub-mode may also be implemented as an independent third mode of operation in the word recognizer element.
  • the invention is a system and associated methods for recognizing words from an utterance containing a succession of one or more spoken words from a predetermined vocabulary.
  • the apparatus includes a word recognizer and a user controlled element for mode selection.
  • the word recognizer is selectively operative in a first mode for determining and displaying a first plurality of words in the vocabulary as a current candidate word list for one of the spoken words, in response to processing a succession of keywords spoken by a user.
  • Each of the keywords is representative of a correspondingly positioned word component in the spoken word.
  • each of the plurality of words in the displayed list begins with the succession of word components represented by the succession of keywords.
  • the word components may be selected from the group consisting of formatives and characters.
  • the word recognizer is also selectively operative in a second mode for identifying at least a portion of the spoken words from the current candidate word list, in response to one or more commands entered by a user.
  • Those commands are representative of the first k characters of the nth word in the current candidate word list, where k and n are integers and the k characters represent the first k characters of the spoken word.
  • the user may enter the commands through auditory signals or through keyboard commands.
  • the word recognizer also determines, in response to the commands entered by a user, a second plurality of words in the vocabulary as the current candidate word list for the spoken word.
  • a second plurality of words in the vocabulary as the current candidate word list for the spoken word.
  • Each of the words in that second plurality begin with the k characters and are displayed as the current candidate word list.
  • the word recognizer in response to one or more commands entered by the user, may identify an additional i characters in the nth word.
  • i is an integer and the i characters represent the first i characters following the k characters previously identified.
  • the word recognizer in response to one or more commands entered by the user, may exclude j characters from the k characters previously identified.
  • j is an integer and the j characters represent the j characters immediately preceding the kth character of the nth word.
  • the word recognizer may also include an element for recognizing the words from the utterance, wherein the utterance includes one or more words spoken as continuous speech.
  • the word recognizer may include an element for recognizing words, wherein the utterance includes one or more words spoken in isolation.
  • the word recognizer may also include a completion element, selectively operative in response to the identification of a portion of the spoken word, for determining the remainder of the spoken word.
  • the completion element may include an element for processing a succession of key words or formatives entered by a user, wherein each of the key words or formatives is representative of a correspondingly positioned character in the remainder of the spoken word, to identify the succession of characters represented by the succession of the keywords or formatives as the remainder of the spoken word.
  • the invention includes a system and methods for recognizing compound words from an utterance containing a succession of one or more words from a predetermined vocabulary where at least one of the words is a compound word, including at least two formatives in succession.
  • the invention includes a processing element, a word recognizer element and a combination element.
  • the processing element is for processing a succession of formatives spoken by a user and for associating, with each of the spoken formatives, a score representative of a likelihood of the spoken formative being included in the compound word.
  • the word recognizer element responsive to those associated scores, identifies as the compound word, those successively spoken formatives having a combined score corresponding to a predetermined criteria.
  • each of the formatives is representative of a correspondingly positioned formative in the compound word.
  • the combination element generates a signal representative of the combination of the successive formatives identified by the word recognizer means.
  • the signal is representative of the compound word.
  • this embodiment includes a user controlled element for decomposing into a signal representative, of the identified formatives, the identified compound word.
  • This user controlled element may be responsive to a user generated auditory signal or to user generated keyboard commands.
  • the invention includes an apparatus and methods for recognizing compound words from an utterance containing a succession of one or more words from a predetermined vocabulary. At least one of the words in the succession is a compound word, including at least two formatives in succession. The formatives are words in the vocabulary.
  • the apparatus includes a user controlled element for generating a signal representative of a compound word, wherein the signal corresponds to selected ones of the formatives uttered in succession.
  • FIG. 1 shows a schematic diagram of a speech recognition system, including a compound word recognizer according to the present invention
  • FIG. 2 is an operational flow diagram further illustrating the operation of the embodiment of FIG. 2;
  • FIG. 3 is a block diagram illustrative of the operation of a preferred embodiment of the present invention in accord with the flow diagram of FIG. 2;
  • FIG. 4 is an operational flow diagram illustrating the operation of another preferred embodiment of the present invention.
  • FIGS. 5-11 depict an example illustrative of the operation of a preferred embodiment of the present invention in accord with FIG. 4.
  • FIG. 1 shows a schematic block diagram of a speech recognition system 10, incorporating a compound word recognizer according to the invention.
  • the system 10 includes elements for detecting an utterance of a spoken word and for generating digital signals representative of that utterance. Those elements include a microphone 14, an analog to digital converter 16, a peak amplitude detector 18, a fast Fourier transform (FFT) network 20 and an utterance detector 22.
  • the signals produced by these elements are supplied to a programmable computer 24, such as an IBM compatible 386 or 486-type personal computer.
  • the computer 24 is equipped with a video monitor 26 for displaying words recognized by the system 10.
  • the computer 24 further includes a keyboard 26 to enable an operator to communicate with the computer 24 by means other than the microphone 14.
  • the monitor 26, the keyboard 28 and their respective interfaces 30 and 32 are of a type commonly used with personal computers.
  • the microphone 14 generates an analog output signal indicative of spoken words entering it.
  • the microphone output signal is connected to the input of the analog to digital converter 16.
  • the analog to digital converter 16 converts the analog signal, produced by the microphone 14, into a sequence of digital values representative of the amplitude of the microphone output signal.
  • the output of the analog to digital converter 16 is supplied to the inputs of the peak amplitude detector 18 and the fast Fourier transform network 20.
  • the fast Fourier transform network 20 which is of a type well known in the art, converts the output of the analog to digital converter 16 into a sequence of frames. Each frame is indicative of the amplitude of the microphone output signal at a particular frequency over a relatively short period of time. According to one embodiment of the invention, the Fourier transform network 20 outputs one frame every fiftieth of a second. This output is supplied, by way of data line 34 and input port 36, to the system bus 38 of computer 24. The Fourier transform network 20 also supplies an interrupt signal on an output line 39. That signal is coupled through the system bus 38 to the central processor unit 40 of computer 24.
  • the central processor unit 40 In response to the interrupt produced by the Fourier transform network 20, the central processor unit 40 reads the data representative of each successive frame, provided on line 34, and stores it in a random access memory 42.
  • the Fourier transform network 20 also supplies the frame clock signals via line 44 to the peak amplitude detector 18.
  • the computer 24 also contains a likelihood processor 46.
  • the likelihood processor 46 is a special-purpose processor designed to determine the words in the system dictionary which are most likely to correspond to a detected word uttered by the user.
  • the system dictionary contains both acoustic word models (i.e., an active library) and alphabetic word listings (i.e., a backup library).
  • the likelihood processor 46 determines probable matches for the word uttered by comparing the data read by the central processor 40 against the pre-defined acoustic models stored in the random access memory 42. According to some prior art systems, the likelihood processor also searches the alphabetic word listings. Acoustic models are not typically stored for words contained in the alphabetic listings.
  • the compound word recognizer 12 is responsible for recognizing compound words spoken by a user. As previously discussed, what constitutes a compound word may differ depending on the particular language in which the system 10 is operating. However, the term compound word, as it is used throughout the present application, refers generally to a word made up of a succession of two or more formatives, each of which is treated as a word in the system vocabulary.
  • Both the likelihood computations and the compound word recognizer computations tend to be processor intensive.
  • the performance of the system 10 may be enhanced by assigning dedicated elements to perform those computations.
  • those computations could just as well be performed by the central processor 40, under software control.
  • the compound word recognizer 12 may also be included as part of the likelihood processor 46.
  • FIG. 2 shows an operational flow diagram 50 according to one embodiment of the invention.
  • the "Normal Mode" block 52 depicted in the flow diagram 50, represents prior art dictation systems. Typically, those systems are incapable of recognizing a compound word unless that word is stored in the system library, either as an acoustic model or as an alphabetic listing.
  • the Normal Mode system In operation, in response to an utterance made up of a succession of words, isolated or continuous, the Normal Mode system generates a candidate word list for each spoken word. As is shown by decision block 54, if a spoken word is on the displayed candidate list, then the word is selected 56 (either manually or automatically) and the system remains in normal dictation mode. However, if, as is likely with compound words, the uttered word is not on the displayed candidate list, the user may select from any one of several options 58. One option, shown by block 60, is for the user to enter a sequence of characters representative of the spoken compound word.
  • This sub-mode of operation is referred to as "Spell Mode.”
  • the definition of a character may vary according to the language in which the user is operating. However, by way of example, in English a user may enter the spelling of the word, i.e., one letter at a time. Likewise, the user may employ the International Communication Alphabet (e.g., "alpha” for "a”, “bravo” for "b”, etc.). The user may then accept the correctly spelled word, as indicated by block 62, and return to the normal dictation mode.
  • a second option, indicated by decision block 64 is for a user to select a mode of operation wherein the particular compound word may be entered by speaking a combination of formatives and/or characters which comprise the compound word.
  • a user speaks a series of formatives to be combined into a compound word. For example, a user might speak “through” followed by "put” to enter the compound word “throughput.”
  • the user may speak one or more characters in combination with one or more formatives which the word recognizer transforms into a compound word. For instance, a user might enter "s" "o” "c” "i” "o” followed by "cultural” to realize the word “sociocultural” and thus, avoid spelling the entire word.
  • the user continues to enter formatives and/or characters until the compound word is completed.
  • the decision block 70 upon completion of the compound word, the user accepts the word, indicated by block 72, and returns to normal dictation mode 52.
  • FIG. 3 shows a block diagram illustrative of the operation of a compound word recognizer system described by the flow diagram 50 of FIG. 2.
  • the system 80 includes a compound word recognizer element 82 and user control element 84.
  • the system 80 detects an input utterance 86, containing one or more formatives F1-F6, spoken either as continuous speech or in isolation.
  • the compound word recognizer determines which of the formatives F1-F6 should be combined to form compound words and in response to that determination, combines those formatives into the proper compound words. This is illustrated by the combinations F1F2, F3F4 and F5F6 shown as output display 88 in FIG. 3.
  • the system 80 operates in any of several user selectable modes.
  • the user selects the particular mode of operation by way of the user control element 84.
  • the user control element 84 may be, for example, the microphone 14 or the keyboard 28, depicted in FIG. 1.
  • a first mode of operation is illustrated by the input formatives F1 and F2.
  • Input formatives F1 and F2 represent a compound word, spoken as normal speech. According to the invention, if the word exists in either the active library or the backup library, the compound word recognizer 82 chooses that word from the library and displays it for the user, much in the same way that a non-compound word is identified and displayed in prior art systems.
  • the word recognizer 82 In a second mode of operation, illustrated by input formatives F3 and F4, the word recognizer 82 combines into a compound word those formatives spoken between a spoken begin compound word delimiter and a spoken end of compound word delimiter.
  • a user might say “begin compound” "F3” "F4" "end compound.”
  • a user may choose to combine both formatives and characters. For instance, the user might say “begin compound” “Arbeit” “Samuel” “Moral” "end compound.” This results in the compound word “urssmoral.” In this example "Samuel” is the German Telephone Alphabet character for the letter “s.” Other codes representative of characters may also be employed.
  • the word recognizer 82 automatically determines whether a series of sequentially spoken formatives should be combined and displayed for the user as a compound word.
  • the word recognizer 82 accomplishes this by processing a succession of detected formatives and associating with each recognized formative, a score representative of the likelihood of the detected formative being included, in a compound word, with either of the formatives detected immediately before or after it. If it is more likely that the formatives should be combined as opposed to existing in isolation, then the word recognizer 82 combines the formatives into a compound word.
  • One technique used to determine the relative likelihoods of whether any group of formatives should exist in isolation or should be combined into a compound word is to use a language model.
  • Language models predict the relative likelihood of the occurrence of each word in the system's vocabulary, given the one or more words which have been spoken previously.
  • the compound word recognizer 82 is responsive to a user command to decompose, into its identified formatives, a previously identified compounded word.
  • FIG. 4 shows an operational flow diagram 100 according to an alternate embodiment of the invention.
  • the "Normal Mode" block 102 depicted in the flow diagram 100, represents prior art dictation systems.
  • decision block 103 if the uttered word is on the candidate list 103, the word is selected and the system remains in normal dictation mode.
  • decision block 106 if the word is not on the candidate list, the user has several options. That choice of options is represented by decision block 106.
  • the user may specify the compound word by way of word components or by specifying portions of words contained on the candidate list. Additionally, the user may complete a partially specified compound word by entering characters.
  • Word components include both formatives and characters. The user enters these formatives and/or characters much in the same as in the embodiment of FIG. 2. However, according to the embodiment of FIG. 4, the compound word recognizer 12 of FIG. 1 automatically generates a candidate list 114 and displays that list 116, in response to the characters and formatives entered by the user. According to one embodiment, the compound word recognizer 12 limits its search of the system library to words that begin with the formatives and/or characters entered by the user. However, in the case of formatives and/or characters, entered through spoken commands, the compound word recognizer may include in its search any formatives and/or characters which it determines has a substantial probability of being the one spoken. By entering additional characters and/or formatives, the user can further limit the scope of the word recognizer's search.
  • decision block 118 if the compound word appears on the choice list, the user selects it 120. If the user selects a word off the candidate list, the system automatically returns to normal dictation mode 102. Alternately, if the word does not appear on the candidate list, the user may once again select any of the several options available from decision block 106.
  • another option available to the user is to select a portion of a word which appears on the candidate list.
  • This option is represented by block 110.
  • specifying a portion of a word on the candidate list can be a time saving alternative to spelling out an entire compound word character by character.
  • the desired portion of a word on the candidate list is selected by specifying the word number (n) and the first (k) characters of the word.
  • the compound word recognizer 12 limits its searching to words in the system library beginning with the selected characters.
  • the word recognizer updates the candidate list accordingly. The updated candidate list is displayed for the user. Once again, if the compound word appears on the list, the user selects it and the system returns to normal dictation mode. If the word does not appear on the candidate list, the system returns to decision block 106 and allows the user to select the particular option to be employed.
  • the user may optionally select additional characters within the word selected from the candidate choice list.
  • the user may also unselect characters previously selected. For example, a user may specify the first three characters of the second word in the candidate list.
  • the candidate list is updated accordingly. If the word is six characters long, the user may then command the system to go forward one, two or three characters, thereby effectively selecting the first four, five or six characters of the word, respectively. Likewise, the user may command the system to go backward one, two or three characters, thereby specifying the first two, one or zero characters in the word, respectively.
  • the candidate choice list is updated with each new selection of characters.
  • a further option available from decision block 106 begins with block 112. This option allows the user to enter one or more characters to complete a compound word. As indicated by blocks 122 and 124, once the complete word has been entered, the user accepts the word and the system returns to the normal dictation mode.
  • FIGS. 5-11 depict an example illustrating the operation of the embodiment of FIGS. 3 and 4.
  • the words "enter and entering” are used to refer both to entering commands by speaking and also by using a keyboard.
  • the user speaks the compound word "thermoregulatory.”
  • the cursor line 130 and the definition window 132 both remain blank.
  • the choice window 134 may remain blank or may simply contain incorrect choices.
  • the user selects between any one of several options. In the example of FIG. 5, the user chooses to enter word components by entering the command "Begin Compound Mode.”
  • the user has the option of entering word components which are characters or those which are formatives.
  • the user enters the formative "therm”.
  • This causes the compound word recognizer to search the dictionary for words beginning with the formative "therm”.
  • "therm" is displayed in the definition window 132.
  • nine likely candidates are displayed in the choice window 134, preferably in an ordered manner based on likelihood of occurrence values.
  • the word recognizer has correctly identified the formative "therm,” under certain conditions the recognizer might determine that there exists a substantial probability that the spoken formative was other than "therm.” For example, the recognizer might identify "term” or "derm” to be equally probable choices. In that situation the word recognizer, according to one preferred embodiment, displays the most likely choices given the probabilities which it has assigned to both the initial spoken word (e.g., thermo regulatory) and the spoken formative (e.g., therm).
  • the initial spoken word e.g., thermo regulatory
  • the spoken formative e.g., therm
  • the user While the desired word is not displayed in the choice window 134, the user has the option of selecting a portion of any of the displayed choices. In this case the user enters the command "select six first six.” This selects the first six letters of the sixth word of the choice list.
  • thermodynamic containing the sixth candidate, thermodynamic, with the first six characters (e.g., "thermo") delineated, is shown at 136 in FIG. 7.
  • the selection of "thermo" causes the compound word recognizer to update the candidate list shown in the choice window 134.
  • all nine choices now begin with the formative "thermo.” Since none of the choices of FIG. 7 have in excess of the first six letters in common with "thermoregulatory,” the user exercises the option to enter word components that are characters.
  • the characters spelled out by the user are placed at the beginning of the word to be identified, unless otherwise specified.
  • the spell character 135 immediately follows a previously spoken formative, such as "therm”
  • the spell character is presumed to follow the spoken formative, unless otherwise specified by a user.
  • the spell character immediately follows a command such as "select six, first six”
  • the spell character is presumed to follow the selected characters (e.g., the seventh character of the sixth word in the present example).
  • the spell character is not preceded by a formative or a select type command, the spell character, as in the prior art systems, is placed at the beginning of the word to be recognized.
  • the user enters the character "r" by way of its corresponding International Communication Alphabet character (e.g., romeo).
  • the International Communication Alphabet is employed to enter West European alphabet characters.
  • different characters/symbols may be used.
  • the compound word recognizer incorporates the "r" into the definition window 132 thereby eliminating the “dynamic” portion of "thermodynamic.”
  • the word recognizer also updates the candidate list displayed in the choice window 134. The user then exercises the option of selecting the first nine letters of the second word.
  • FIG. 9 shows the word “thermoregulation” in the definition window 132 with the letters “thermoreg” delineated at 136.
  • the choice window contains the only candidate having "thermoreg” as its first nine characters. Realizing that the second selection of FIG. 8, still shown in the definition window 132 of FIG. 9, has in excess of the first nine letters in common with the desired compound word, the user enters the command "forward four.”

Landscapes

  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Machine Translation (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Selective Calling Equipment (AREA)
US08/437,965 1992-03-06 1995-05-10 Speech recognition system for languages with compound words Expired - Lifetime US5754972A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US08/437,965 US5754972A (en) 1992-03-06 1995-05-10 Speech recognition system for languages with compound words

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US84669592A 1992-03-06 1992-03-06
US08/437,965 US5754972A (en) 1992-03-06 1995-05-10 Speech recognition system for languages with compound words

Related Parent Applications (1)

Application Number Title Priority Date Filing Date
US84669592A Continuation 1992-03-06 1992-03-06

Publications (1)

Publication Number Publication Date
US5754972A true US5754972A (en) 1998-05-19

Family

ID=25298677

Family Applications (1)

Application Number Title Priority Date Filing Date
US08/437,965 Expired - Lifetime US5754972A (en) 1992-03-06 1995-05-10 Speech recognition system for languages with compound words

Country Status (6)

Country Link
US (1) US5754972A (fr)
EP (1) EP0634042B1 (fr)
JP (1) JP3414735B2 (fr)
AT (1) ATE203119T1 (fr)
DE (1) DE69330427T2 (fr)
WO (1) WO1993018506A1 (fr)

Cited By (66)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0992979A2 (fr) * 1998-09-30 2000-04-12 Dragon Systems Inc. Reconnaissance de mots composés
US6088671A (en) * 1995-11-13 2000-07-11 Dragon Systems Continuous speech recognition of text and commands
US6108631A (en) * 1997-09-24 2000-08-22 U.S. Philips Corporation Input system for at least location and/or street names
US6122613A (en) * 1997-01-30 2000-09-19 Dragon Systems, Inc. Speech recognition using multiple recognizers (selectively) applied to the same input sample
US6163768A (en) * 1998-06-15 2000-12-19 Dragon Systems, Inc. Non-interactive enrollment in speech recognition
US6195635B1 (en) 1998-08-13 2001-02-27 Dragon Systems, Inc. User-cued speech recognition
US6224383B1 (en) * 1999-03-25 2001-05-01 Planetlingo, Inc. Method and system for computer assisted natural language instruction with distracters
WO2001061683A1 (fr) * 2000-02-16 2001-08-23 Pc Multimedia Limited Identification de structures dans des sequences temporelles de donnees
US20010053975A1 (en) * 2000-06-14 2001-12-20 Nec Corporation Character information receiving apparatus
US6349282B1 (en) * 1999-04-20 2002-02-19 Larnout & Hauspie Speech Products N.V. Compound words in speech recognition systems
US6374218B2 (en) * 1997-08-08 2002-04-16 Fujitsu Limited Speech recognition system which displays a subject for recognizing an inputted voice
US6490561B1 (en) * 1997-06-25 2002-12-03 Dennis L. Wilson Continuous speech voice transcription
US20030097252A1 (en) * 2001-10-18 2003-05-22 Mackie Andrew William Method and apparatus for efficient segmentation of compound words using probabilistic breakpoint traversal
US6577999B1 (en) * 1999-03-08 2003-06-10 International Business Machines Corporation Method and apparatus for intelligently managing multiple pronunciations for a speech recognition vocabulary
US6601027B1 (en) 1995-11-13 2003-07-29 Scansoft, Inc. Position manipulation in speech recognition
US20040049388A1 (en) * 2001-09-05 2004-03-11 Roth Daniel L. Methods, systems, and programming for performing speech recognition
US6708150B1 (en) * 1999-09-09 2004-03-16 Zanavi Informatics Corporation Speech recognition apparatus and speech recognition navigation apparatus
US6754617B1 (en) * 2000-07-06 2004-06-22 Hapax Limited Method for automatically determining solid compound words
US20040254784A1 (en) * 2003-02-12 2004-12-16 International Business Machines Corporation Morphological analyzer, natural language processor, morphological analysis method and program
US20050033565A1 (en) * 2003-07-02 2005-02-10 Philipp Koehn Empirical methods for splitting compound words with application to machine translation
US20050043949A1 (en) * 2001-09-05 2005-02-24 Voice Signal Technologies, Inc. Word recognition using choice lists
US20050043947A1 (en) * 2001-09-05 2005-02-24 Voice Signal Technologies, Inc. Speech recognition using ambiguous or phone key spelling and/or filtering
US20050049880A1 (en) * 2001-09-05 2005-03-03 Voice Signal Technologies, Inc. Speech recognition using selectable recognition modes
US20050075874A1 (en) * 2003-10-01 2005-04-07 International Business Machines Corporation Relative delta computations for determining the meaning of language inputs
ES2237345A1 (es) * 2005-02-28 2005-07-16 Prous Science S.A. Procedimiento de conversion de fonemas a texto escrito y sistema informatico y programa informatico correspondientes.
US20050159948A1 (en) * 2001-09-05 2005-07-21 Voice Signal Technologies, Inc. Combined speech and handwriting recognition
US20050159957A1 (en) * 2001-09-05 2005-07-21 Voice Signal Technologies, Inc. Combined speech recognition and sound recording
US20060106612A1 (en) * 1998-05-01 2006-05-18 Ben Franklin Patent Holding Llc Voice user interface with personality
US7120582B1 (en) 1999-09-07 2006-10-10 Dragon Systems, Inc. Expanding an effective vocabulary of a speech recognition system
US7289957B1 (en) * 1999-10-28 2007-10-30 Siemens Aktiengesellschaft Verifying a speaker using random combinations of speaker's previously-supplied syllable units
US7444286B2 (en) 2001-09-05 2008-10-28 Roth Daniel L Speech recognition using re-utterance recognition
EP2006835A1 (fr) 2007-06-20 2008-12-24 Siemens Aktiengesellschaft Procédé destiné à la création d'une liste d'hypothèses à l'aide du vocabulaire d'un système de reconnaissance vocale
US20100042398A1 (en) * 2002-03-26 2010-02-18 Daniel Marcu Building A Translation Lexicon From Comparable, Non-Parallel Corpora
US7945445B1 (en) * 2000-07-14 2011-05-17 Svox Ag Hybrid lexicon for speech recognition
US8214196B2 (en) 2001-07-03 2012-07-03 University Of Southern California Syntax-based statistical translation model
US8296127B2 (en) 2004-03-23 2012-10-23 University Of Southern California Discovery of parallel text portions in comparable collections of corpora and training using comparable texts
US8380486B2 (en) 2009-10-01 2013-02-19 Language Weaver, Inc. Providing machine-generated translations and corresponding trust levels
US20130090919A1 (en) * 2011-10-11 2013-04-11 Casio Computer Co., Ltd. Electronic device and dictionary data display method
US8433556B2 (en) 2006-11-02 2013-04-30 University Of Southern California Semi-supervised training for statistical word alignment
US8468149B1 (en) 2007-01-26 2013-06-18 Language Weaver, Inc. Multi-lingual online community
US8548794B2 (en) 2003-07-02 2013-10-01 University Of Southern California Statistical noun phrase translation
US20130275123A1 (en) * 2004-08-31 2013-10-17 Research In Motion Limited Handheld electronic device with text disambiguation
US8600728B2 (en) 2004-10-12 2013-12-03 University Of Southern California Training for a text-to-text application which uses string to tree conversion for training and decoding
US8615389B1 (en) 2007-03-16 2013-12-24 Language Weaver, Inc. Generation and exploitation of an approximate language model
US8666725B2 (en) 2004-04-16 2014-03-04 University Of Southern California Selection and use of nonstatistical translation components in a statistical machine translation framework
US8676563B2 (en) 2009-10-01 2014-03-18 Language Weaver, Inc. Providing human-generated and machine-generated trusted translations
US8694303B2 (en) 2011-06-15 2014-04-08 Language Weaver, Inc. Systems and methods for tuning parameters in statistical machine translation
US8825466B1 (en) 2007-06-08 2014-09-02 Language Weaver, Inc. Modification of annotated bilingual segment pairs in syntax-based machine translation
US8831928B2 (en) 2007-04-04 2014-09-09 Language Weaver, Inc. Customizable machine translation service
US8886517B2 (en) 2005-06-17 2014-11-11 Language Weaver, Inc. Trust scoring for language translation systems
US8886518B1 (en) 2006-08-07 2014-11-11 Language Weaver, Inc. System and method for capitalizing machine translated text
US8886515B2 (en) 2011-10-19 2014-11-11 Language Weaver, Inc. Systems and methods for enhancing machine translation post edit review processes
US8942973B2 (en) 2012-03-09 2015-01-27 Language Weaver, Inc. Content page URL translation
US8943080B2 (en) 2006-04-07 2015-01-27 University Of Southern California Systems and methods for identifying parallel documents and sentence fragments in multilingual document collections
US8990064B2 (en) 2009-07-28 2015-03-24 Language Weaver, Inc. Translating documents based on content
US9122674B1 (en) 2006-12-15 2015-09-01 Language Weaver, Inc. Use of annotations in statistical machine translation
US20150255069A1 (en) * 2014-03-04 2015-09-10 Amazon Technologies, Inc. Predicting pronunciation in speech recognition
US9152622B2 (en) 2012-11-26 2015-10-06 Language Weaver, Inc. Personalized machine translation via online adaptation
US9213694B2 (en) 2013-10-10 2015-12-15 Language Weaver, Inc. Efficient online domain adaptation
US20160336004A1 (en) * 2015-05-14 2016-11-17 Nuance Communications, Inc. System and method for processing out of vocabulary compound words
US20180060282A1 (en) * 2016-08-31 2018-03-01 Nuance Communications, Inc. User interface for dictation application employing automatic speech recognition
US10261994B2 (en) 2012-05-25 2019-04-16 Sdl Inc. Method and system for automatic management of reputation of translators
US10319252B2 (en) 2005-11-09 2019-06-11 Sdl Inc. Language capability assessment and training apparatus and techniques
US20190214018A1 (en) * 2018-01-09 2019-07-11 Sennheiser Electronic Gmbh & Co. Kg Method for speech processing and speech processing device
US10417646B2 (en) 2010-03-09 2019-09-17 Sdl Inc. Predicting the cost associated with translating textual content
US11003838B2 (en) 2011-04-18 2021-05-11 Sdl Inc. Systems and methods for monitoring post translation editing

Families Citing this family (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19510083C2 (de) * 1995-03-20 1997-04-24 Ibm Verfahren und Anordnung zur Spracherkennung bei Wortkomposita enthaltenden Sprachen
US6044337A (en) * 1997-10-29 2000-03-28 At&T Corp Selection of superwords based on criteria relevant to both speech recognition and understanding
EP1135768B1 (fr) * 1999-09-29 2008-01-23 Koninklijke Philips Electronics N.V. Mode de verification orthographique dans un systeme de reconnaissance vocale
WO2012073275A1 (fr) 2010-11-30 2012-06-07 三菱電機株式会社 Dispositif de reconnaissance vocale et dispositif de navigation
US9721563B2 (en) 2012-06-08 2017-08-01 Apple Inc. Name recognition system
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
US10567477B2 (en) 2015-03-08 2020-02-18 Apple Inc. Virtual assistant continuity
US9578173B2 (en) 2015-06-05 2017-02-21 Apple Inc. Virtual assistant aided communication with 3rd party service in a communication session
US10446143B2 (en) 2016-03-14 2019-10-15 Apple Inc. Identification of voice inputs providing credentials
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
DK201770439A1 (en) 2017-05-11 2018-12-13 Apple Inc. Offline personal assistant
DK179496B1 (en) 2017-05-12 2019-01-15 Apple Inc. USER-SPECIFIC Acoustic Models
DK179745B1 (en) 2017-05-12 2019-05-01 Apple Inc. SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT
DK201770431A1 (en) 2017-05-15 2018-12-20 Apple Inc. Optimizing dialogue policy decisions for digital assistants using implicit feedback
DK201770432A1 (en) 2017-05-15 2018-12-21 Apple Inc. Hierarchical belief states for digital assistants
DK179549B1 (en) 2017-05-16 2019-02-12 Apple Inc. FAR-FIELD EXTENSION FOR DIGITAL ASSISTANT SERVICES

Citations (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3647978A (en) * 1969-04-30 1972-03-07 Int Standard Electric Corp Speech recognition apparatus
GB2082820A (en) * 1980-08-20 1982-03-10 Neff Gregor Nicholas Devices, Systems and Methods for Converting Speech into Corresponding Written Form
JPS5775349A (en) * 1980-10-28 1982-05-11 Nippon Telegr & Teleph Corp <Ntt> Japanese input device of voice recognition type
US4624008A (en) * 1983-03-09 1986-11-18 International Telephone And Telegraph Corporation Apparatus for automatic speech recognition
US4718094A (en) * 1984-11-19 1988-01-05 International Business Machines Corp. Speech recognition system
US4749353A (en) * 1982-05-13 1988-06-07 Texas Instruments Incorporated Talking electronic learning aid for improvement of spelling with operator-controlled word list
US4783803A (en) * 1985-11-12 1988-11-08 Dragon Systems, Inc. Speech recognition apparatus and method
US4803729A (en) * 1987-04-03 1989-02-07 Dragon Systems, Inc. Speech recognition method
US4805218A (en) * 1987-04-03 1989-02-14 Dragon Systems, Inc. Method for speech analysis and speech recognition
US4805219A (en) * 1987-04-03 1989-02-14 Dragon Systems, Inc. Method for speech recognition
US4829576A (en) * 1986-10-21 1989-05-09 Dragon Systems, Inc. Voice recognition system
US4829578A (en) * 1986-10-02 1989-05-09 Dragon Systems, Inc. Speech detection and recognition apparatus for use with background noise of varying levels
US4837831A (en) * 1986-10-15 1989-06-06 Dragon Systems, Inc. Method for creating and using multiple-word sound models in speech recognition
US4866778A (en) * 1986-08-11 1989-09-12 Dragon Systems, Inc. Interactive speech recognition apparatus
US4868750A (en) * 1987-10-07 1989-09-19 Houghton Mifflin Company Collocational grammar system
US4903305A (en) * 1986-05-12 1990-02-20 Dragon Systems, Inc. Method for representing word models for use in speech recognition
US4914703A (en) * 1986-12-05 1990-04-03 Dragon Systems, Inc. Method for deriving acoustic models for use in speech recognition
US4914704A (en) * 1984-10-30 1990-04-03 International Business Machines Corporation Text editor for speech input
US4947438A (en) * 1987-07-11 1990-08-07 U.S. Philips Corporation Process for the recognition of a continuous flow of spoken words
US4959864A (en) * 1985-02-07 1990-09-25 U.S. Philips Corporation Method and system for providing adaptive interactive command response
US4984178A (en) * 1989-02-21 1991-01-08 Texas Instruments Incorporated Chart parser for stochastic unification grammar
US5005203A (en) * 1987-04-03 1991-04-02 U.S. Philips Corporation Method of recognizing continuously spoken words
US5027406A (en) * 1988-12-06 1991-06-25 Dragon Systems, Inc. Method for interactive speech recognition and training
US5027408A (en) * 1987-04-09 1991-06-25 Kroeker John P Speech-recognition circuitry employing phoneme estimation
US5065318A (en) * 1989-04-24 1991-11-12 Sharp Kabushiki Kaisha Method of translating a sentence including a compound word formed by hyphenation using a translating apparatus
US5164900A (en) * 1983-11-14 1992-11-17 Colman Bernath Method and device for phonetically encoding Chinese textual data for data processing entry
US5225981A (en) * 1986-10-03 1993-07-06 Ricoh Company, Ltd. Language analyzer for morphemically and syntactically analyzing natural languages by using block analysis and composite morphemes

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH06103457B2 (ja) * 1985-12-27 1994-12-14 株式会社日立製作所 音声タイプライタ
JPS6373300A (ja) * 1986-09-17 1988-04-02 キヤノン株式会社 音声認識装置
JP2585547B2 (ja) * 1986-09-19 1997-02-26 株式会社日立製作所 音声入出力装置における入力音声の修正方法
JPH03179498A (ja) * 1989-12-08 1991-08-05 Nippon Telegr & Teleph Corp <Ntt> 音声日本語変換方式

Patent Citations (27)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3647978A (en) * 1969-04-30 1972-03-07 Int Standard Electric Corp Speech recognition apparatus
GB2082820A (en) * 1980-08-20 1982-03-10 Neff Gregor Nicholas Devices, Systems and Methods for Converting Speech into Corresponding Written Form
JPS5775349A (en) * 1980-10-28 1982-05-11 Nippon Telegr & Teleph Corp <Ntt> Japanese input device of voice recognition type
US4749353A (en) * 1982-05-13 1988-06-07 Texas Instruments Incorporated Talking electronic learning aid for improvement of spelling with operator-controlled word list
US4624008A (en) * 1983-03-09 1986-11-18 International Telephone And Telegraph Corporation Apparatus for automatic speech recognition
US5164900A (en) * 1983-11-14 1992-11-17 Colman Bernath Method and device for phonetically encoding Chinese textual data for data processing entry
US4914704A (en) * 1984-10-30 1990-04-03 International Business Machines Corporation Text editor for speech input
US4718094A (en) * 1984-11-19 1988-01-05 International Business Machines Corp. Speech recognition system
US4959864A (en) * 1985-02-07 1990-09-25 U.S. Philips Corporation Method and system for providing adaptive interactive command response
US4783803A (en) * 1985-11-12 1988-11-08 Dragon Systems, Inc. Speech recognition apparatus and method
US4903305A (en) * 1986-05-12 1990-02-20 Dragon Systems, Inc. Method for representing word models for use in speech recognition
US4866778A (en) * 1986-08-11 1989-09-12 Dragon Systems, Inc. Interactive speech recognition apparatus
US4829578A (en) * 1986-10-02 1989-05-09 Dragon Systems, Inc. Speech detection and recognition apparatus for use with background noise of varying levels
US5225981A (en) * 1986-10-03 1993-07-06 Ricoh Company, Ltd. Language analyzer for morphemically and syntactically analyzing natural languages by using block analysis and composite morphemes
US4837831A (en) * 1986-10-15 1989-06-06 Dragon Systems, Inc. Method for creating and using multiple-word sound models in speech recognition
US4829576A (en) * 1986-10-21 1989-05-09 Dragon Systems, Inc. Voice recognition system
US4914703A (en) * 1986-12-05 1990-04-03 Dragon Systems, Inc. Method for deriving acoustic models for use in speech recognition
US4805219A (en) * 1987-04-03 1989-02-14 Dragon Systems, Inc. Method for speech recognition
US5005203A (en) * 1987-04-03 1991-04-02 U.S. Philips Corporation Method of recognizing continuously spoken words
US4805218A (en) * 1987-04-03 1989-02-14 Dragon Systems, Inc. Method for speech analysis and speech recognition
US4803729A (en) * 1987-04-03 1989-02-07 Dragon Systems, Inc. Speech recognition method
US5027408A (en) * 1987-04-09 1991-06-25 Kroeker John P Speech-recognition circuitry employing phoneme estimation
US4947438A (en) * 1987-07-11 1990-08-07 U.S. Philips Corporation Process for the recognition of a continuous flow of spoken words
US4868750A (en) * 1987-10-07 1989-09-19 Houghton Mifflin Company Collocational grammar system
US5027406A (en) * 1988-12-06 1991-06-25 Dragon Systems, Inc. Method for interactive speech recognition and training
US4984178A (en) * 1989-02-21 1991-01-08 Texas Instruments Incorporated Chart parser for stochastic unification grammar
US5065318A (en) * 1989-04-24 1991-11-12 Sharp Kabushiki Kaisha Method of translating a sentence including a compound word formed by hyphenation using a translating apparatus

Non-Patent Citations (16)

* Cited by examiner, † Cited by third party
Title
Bahl, et al., IEEE, Feb., 1989, pp. 345 347, Matrix Fast Match: A Fast Method for Identifying a Short List of Candidate Words for Decoding . *
Bahl, et al., IEEE, Feb., 1989, pp. 345-347, "Matrix Fast Match: A Fast Method for Identifying a Short List of Candidate Words for Decoding".
Bahl, et al., IEEE, Feb., 1990, pp. 85 88, Constructing Graps of Aconstically Confusable Words . *
Bahl, et al., IEEE, Feb., 1990, pp. 85-88, "Constructing Graps of Aconstically Confusable Words".
Bahl, et al., IEEE, Sep., 1988, pp. 489 492, Obtaining Candidate Words by Polling in a Large Vocabulary Speech Recognition System. *
Bahl, et al., IEEE, Sep., 1988, pp. 489-492, "Obtaining Candidate Words by Polling in a Large Vocabulary Speech Recognition System."
Bandara et al. "Handling German compound words in an isolated-word speech recognizer"--Contribution to 1991 IEEE Workshop on Speech Recognition, Arden House, Harriman, NY, Dec. 15-18.
Bandara et al. Handling German compound words in an isolated word speech recognizer Contribution to 1991 IEEE Workshop on Speech Recognition, Arden House, Harriman, NY, Dec. 15 18. *
Kenji et al. (1991) "Processing Unknown Words in Continuous Speech Recognition"IEICE Transactions E74(7): 1811-1815.
Kenji et al. (1991) Processing Unknown Words in Continuous Speech Recognition IEICE Transactions E74(7): 1811 1815. *
Patent Abstracts of Japan, vol. 6(159)(P 136), 20 Aug. 1982 & JP A 57 075349 (Nippon Telegraph & Telephone) 11 May 1982, abstract. *
Patent Abstracts of Japan, vol. 6(159)(P-136), 20 Aug. 1982 & JP-A-57 075349 (Nippon Telegraph & Telephone) 11 May 1982, abstract.
PCT International Search Report, Mailed 03 Jun. 1993, for prosecution of PCT/US93/02232. *
Supplementary Search Report, EP 93 90 7431, Dec. 19, 1995. *
Zachary, The Wall Street Journal, Feb. 24, 1992, pp. A3, A7, Apple Unveils `Breakthrough`In Technology : Macintosh.
Zachary, The Wall Street Journal, Feb. 24, 1992, pp. A3, A7, Apple Unveils Breakthrough In Technology : Macintosh. *

Cited By (102)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6601027B1 (en) 1995-11-13 2003-07-29 Scansoft, Inc. Position manipulation in speech recognition
US6088671A (en) * 1995-11-13 2000-07-11 Dragon Systems Continuous speech recognition of text and commands
US6122613A (en) * 1997-01-30 2000-09-19 Dragon Systems, Inc. Speech recognition using multiple recognizers (selectively) applied to the same input sample
US6490561B1 (en) * 1997-06-25 2002-12-03 Dennis L. Wilson Continuous speech voice transcription
US6374218B2 (en) * 1997-08-08 2002-04-16 Fujitsu Limited Speech recognition system which displays a subject for recognizing an inputted voice
US6108631A (en) * 1997-09-24 2000-08-22 U.S. Philips Corporation Input system for at least location and/or street names
US7266499B2 (en) 1998-05-01 2007-09-04 Ben Franklin Patent Holding Llc Voice user interface with personality
US9055147B2 (en) 1998-05-01 2015-06-09 Intellectual Ventures I Llc Voice user interface with personality
US20080103777A1 (en) * 1998-05-01 2008-05-01 Ben Franklin Patent Holding Llc Voice User Interface With Personality
US20060106612A1 (en) * 1998-05-01 2006-05-18 Ben Franklin Patent Holding Llc Voice user interface with personality
US6163768A (en) * 1998-06-15 2000-12-19 Dragon Systems, Inc. Non-interactive enrollment in speech recognition
US6424943B1 (en) 1998-06-15 2002-07-23 Scansoft, Inc. Non-interactive enrollment in speech recognition
US6195635B1 (en) 1998-08-13 2001-02-27 Dragon Systems, Inc. User-cued speech recognition
US6393399B1 (en) * 1998-09-30 2002-05-21 Scansoft, Inc. Compound word recognition
EP0992979A3 (fr) * 1998-09-30 2000-05-31 Dragon Systems Inc. Reconnaissance de mots composés
EP0992979A2 (fr) * 1998-09-30 2000-04-12 Dragon Systems Inc. Reconnaissance de mots composés
US6577999B1 (en) * 1999-03-08 2003-06-10 International Business Machines Corporation Method and apparatus for intelligently managing multiple pronunciations for a speech recognition vocabulary
US6224383B1 (en) * 1999-03-25 2001-05-01 Planetlingo, Inc. Method and system for computer assisted natural language instruction with distracters
US6349282B1 (en) * 1999-04-20 2002-02-19 Larnout & Hauspie Speech Products N.V. Compound words in speech recognition systems
US7120582B1 (en) 1999-09-07 2006-10-10 Dragon Systems, Inc. Expanding an effective vocabulary of a speech recognition system
US6708150B1 (en) * 1999-09-09 2004-03-16 Zanavi Informatics Corporation Speech recognition apparatus and speech recognition navigation apparatus
US7289957B1 (en) * 1999-10-28 2007-10-30 Siemens Aktiengesellschaft Verifying a speaker using random combinations of speaker's previously-supplied syllable units
WO2001061683A1 (fr) * 2000-02-16 2001-08-23 Pc Multimedia Limited Identification de structures dans des sequences temporelles de donnees
US20010053975A1 (en) * 2000-06-14 2001-12-20 Nec Corporation Character information receiving apparatus
US6937987B2 (en) * 2000-06-14 2005-08-30 Nec Corporation Character information receiving apparatus
US6754617B1 (en) * 2000-07-06 2004-06-22 Hapax Limited Method for automatically determining solid compound words
US7945445B1 (en) * 2000-07-14 2011-05-17 Svox Ag Hybrid lexicon for speech recognition
US8214196B2 (en) 2001-07-03 2012-07-03 University Of Southern California Syntax-based statistical translation model
US7225130B2 (en) * 2001-09-05 2007-05-29 Voice Signal Technologies, Inc. Methods, systems, and programming for performing speech recognition
US20050043949A1 (en) * 2001-09-05 2005-02-24 Voice Signal Technologies, Inc. Word recognition using choice lists
US20050159948A1 (en) * 2001-09-05 2005-07-21 Voice Signal Technologies, Inc. Combined speech and handwriting recognition
US20050159957A1 (en) * 2001-09-05 2005-07-21 Voice Signal Technologies, Inc. Combined speech recognition and sound recording
US7809574B2 (en) 2001-09-05 2010-10-05 Voice Signal Technologies Inc. Word recognition using choice lists
US7444286B2 (en) 2001-09-05 2008-10-28 Roth Daniel L Speech recognition using re-utterance recognition
US20050049880A1 (en) * 2001-09-05 2005-03-03 Voice Signal Technologies, Inc. Speech recognition using selectable recognition modes
US20040267528A9 (en) * 2001-09-05 2004-12-30 Roth Daniel L. Methods, systems, and programming for performing speech recognition
US20050043947A1 (en) * 2001-09-05 2005-02-24 Voice Signal Technologies, Inc. Speech recognition using ambiguous or phone key spelling and/or filtering
US7467089B2 (en) 2001-09-05 2008-12-16 Roth Daniel L Combined speech and handwriting recognition
US20040049388A1 (en) * 2001-09-05 2004-03-11 Roth Daniel L. Methods, systems, and programming for performing speech recognition
US7313526B2 (en) * 2001-09-05 2007-12-25 Voice Signal Technologies, Inc. Speech recognition using selectable recognition modes
US7526431B2 (en) 2001-09-05 2009-04-28 Voice Signal Technologies, Inc. Speech recognition using ambiguous or phone key spelling and/or filtering
US7505911B2 (en) 2001-09-05 2009-03-17 Roth Daniel L Combined speech recognition and sound recording
US20030097252A1 (en) * 2001-10-18 2003-05-22 Mackie Andrew William Method and apparatus for efficient segmentation of compound words using probabilistic breakpoint traversal
US7610189B2 (en) * 2001-10-18 2009-10-27 Nuance Communications, Inc. Method and apparatus for efficient segmentation of compound words using probabilistic breakpoint traversal
US20100042398A1 (en) * 2002-03-26 2010-02-18 Daniel Marcu Building A Translation Lexicon From Comparable, Non-Parallel Corpora
US8234106B2 (en) 2002-03-26 2012-07-31 University Of Southern California Building a translation lexicon from comparable, non-parallel corpora
WO2004023455A3 (fr) * 2002-09-06 2006-03-23 Voice Signal Technologies Inc Procedes, systemes et programmation destines a la realisation de reconnaissance vocale
WO2004023455A2 (fr) * 2002-09-06 2004-03-18 Voice Signal Technologies, Inc. Procedes, systemes et programmation destines a la realisation de reconnaissance vocale
US20040254784A1 (en) * 2003-02-12 2004-12-16 International Business Machines Corporation Morphological analyzer, natural language processor, morphological analysis method and program
US7684975B2 (en) * 2003-02-12 2010-03-23 International Business Machines Corporation Morphological analyzer, natural language processor, morphological analysis method and program
US7711545B2 (en) * 2003-07-02 2010-05-04 Language Weaver, Inc. Empirical methods for splitting compound words with application to machine translation
US8548794B2 (en) 2003-07-02 2013-10-01 University Of Southern California Statistical noun phrase translation
US20050033565A1 (en) * 2003-07-02 2005-02-10 Philipp Koehn Empirical methods for splitting compound words with application to machine translation
US7562016B2 (en) 2003-10-01 2009-07-14 Nuance Communications, Inc. Relative delta computations for determining the meaning of language inputs
US8630856B2 (en) 2003-10-01 2014-01-14 Nuance Communications, Inc. Relative delta computations for determining the meaning of language inputs
US20050075874A1 (en) * 2003-10-01 2005-04-07 International Business Machines Corporation Relative delta computations for determining the meaning of language inputs
US20100010805A1 (en) * 2003-10-01 2010-01-14 Nuance Communications, Inc. Relative delta computations for determining the meaning of language inputs
US7366666B2 (en) * 2003-10-01 2008-04-29 International Business Machines Corporation Relative delta computations for determining the meaning of language inputs
US20080147390A1 (en) * 2003-10-01 2008-06-19 International Business Machines Corporation Relative delta computations for determining the meaning of language inputs
US8296127B2 (en) 2004-03-23 2012-10-23 University Of Southern California Discovery of parallel text portions in comparable collections of corpora and training using comparable texts
US8977536B2 (en) 2004-04-16 2015-03-10 University Of Southern California Method and system for translating information with a higher probability of a correct translation
US8666725B2 (en) 2004-04-16 2014-03-04 University Of Southern California Selection and use of nonstatistical translation components in a statistical machine translation framework
US20100174524A1 (en) * 2004-07-02 2010-07-08 Philipp Koehn Empirical Methods for Splitting Compound Words with Application to Machine Translation
US8768685B2 (en) * 2004-08-31 2014-07-01 Blackberry Limited Handheld electronic device with text disambiguation
US20130275123A1 (en) * 2004-08-31 2013-10-17 Research In Motion Limited Handheld electronic device with text disambiguation
US9588596B2 (en) 2004-08-31 2017-03-07 Blackberry Limited Handheld electronic device with text disambiguation
US8600728B2 (en) 2004-10-12 2013-12-03 University Of Southern California Training for a text-to-text application which uses string to tree conversion for training and decoding
ES2237345A1 (es) * 2005-02-28 2005-07-16 Prous Science S.A. Procedimiento de conversion de fonemas a texto escrito y sistema informatico y programa informatico correspondientes.
US8886517B2 (en) 2005-06-17 2014-11-11 Language Weaver, Inc. Trust scoring for language translation systems
US10319252B2 (en) 2005-11-09 2019-06-11 Sdl Inc. Language capability assessment and training apparatus and techniques
US8943080B2 (en) 2006-04-07 2015-01-27 University Of Southern California Systems and methods for identifying parallel documents and sentence fragments in multilingual document collections
US8886518B1 (en) 2006-08-07 2014-11-11 Language Weaver, Inc. System and method for capitalizing machine translated text
US8433556B2 (en) 2006-11-02 2013-04-30 University Of Southern California Semi-supervised training for statistical word alignment
US9122674B1 (en) 2006-12-15 2015-09-01 Language Weaver, Inc. Use of annotations in statistical machine translation
US8468149B1 (en) 2007-01-26 2013-06-18 Language Weaver, Inc. Multi-lingual online community
US8615389B1 (en) 2007-03-16 2013-12-24 Language Weaver, Inc. Generation and exploitation of an approximate language model
US8831928B2 (en) 2007-04-04 2014-09-09 Language Weaver, Inc. Customizable machine translation service
US8825466B1 (en) 2007-06-08 2014-09-02 Language Weaver, Inc. Modification of annotated bilingual segment pairs in syntax-based machine translation
EP2006835A1 (fr) 2007-06-20 2008-12-24 Siemens Aktiengesellschaft Procédé destiné à la création d'une liste d'hypothèses à l'aide du vocabulaire d'un système de reconnaissance vocale
US8990064B2 (en) 2009-07-28 2015-03-24 Language Weaver, Inc. Translating documents based on content
US8380486B2 (en) 2009-10-01 2013-02-19 Language Weaver, Inc. Providing machine-generated translations and corresponding trust levels
US8676563B2 (en) 2009-10-01 2014-03-18 Language Weaver, Inc. Providing human-generated and machine-generated trusted translations
US10984429B2 (en) 2010-03-09 2021-04-20 Sdl Inc. Systems and methods for translating textual content
US10417646B2 (en) 2010-03-09 2019-09-17 Sdl Inc. Predicting the cost associated with translating textual content
US11003838B2 (en) 2011-04-18 2021-05-11 Sdl Inc. Systems and methods for monitoring post translation editing
US8694303B2 (en) 2011-06-15 2014-04-08 Language Weaver, Inc. Systems and methods for tuning parameters in statistical machine translation
US20130090919A1 (en) * 2011-10-11 2013-04-11 Casio Computer Co., Ltd. Electronic device and dictionary data display method
US9208143B2 (en) * 2011-10-11 2015-12-08 Casio Computer Co., Ltd. Electronic device and dictionary data display method
US8886515B2 (en) 2011-10-19 2014-11-11 Language Weaver, Inc. Systems and methods for enhancing machine translation post edit review processes
US8942973B2 (en) 2012-03-09 2015-01-27 Language Weaver, Inc. Content page URL translation
US10402498B2 (en) 2012-05-25 2019-09-03 Sdl Inc. Method and system for automatic management of reputation of translators
US10261994B2 (en) 2012-05-25 2019-04-16 Sdl Inc. Method and system for automatic management of reputation of translators
US9152622B2 (en) 2012-11-26 2015-10-06 Language Weaver, Inc. Personalized machine translation via online adaptation
US9213694B2 (en) 2013-10-10 2015-12-15 Language Weaver, Inc. Efficient online domain adaptation
US10339920B2 (en) * 2014-03-04 2019-07-02 Amazon Technologies, Inc. Predicting pronunciation in speech recognition
US20150255069A1 (en) * 2014-03-04 2015-09-10 Amazon Technologies, Inc. Predicting pronunciation in speech recognition
US10380242B2 (en) * 2015-05-14 2019-08-13 Nuance Communications, Inc. System and method for processing out of vocabulary compound words
US20160336004A1 (en) * 2015-05-14 2016-11-17 Nuance Communications, Inc. System and method for processing out of vocabulary compound words
US20180060282A1 (en) * 2016-08-31 2018-03-01 Nuance Communications, Inc. User interface for dictation application employing automatic speech recognition
US10706210B2 (en) * 2016-08-31 2020-07-07 Nuance Communications, Inc. User interface for dictation application employing automatic speech recognition
US20190214018A1 (en) * 2018-01-09 2019-07-11 Sennheiser Electronic Gmbh & Co. Kg Method for speech processing and speech processing device
US10861463B2 (en) * 2018-01-09 2020-12-08 Sennheiser Electronic Gmbh & Co. Kg Method for speech processing and speech processing device

Also Published As

Publication number Publication date
DE69330427D1 (de) 2001-08-16
JPH07507880A (ja) 1995-08-31
EP0634042B1 (fr) 2001-07-11
DE69330427T2 (de) 2002-05-23
WO1993018506A1 (fr) 1993-09-16
JP3414735B2 (ja) 2003-06-09
ATE203119T1 (de) 2001-07-15
EP0634042A4 (fr) 1996-02-21
EP0634042A1 (fr) 1995-01-18

Similar Documents

Publication Publication Date Title
US5754972A (en) Speech recognition system for languages with compound words
US6363347B1 (en) Method and system for displaying a variable number of alternative words during speech recognition
US5829000A (en) Method and system for correcting misrecognized spoken words or phrases
US5884258A (en) Method and system for editing phrases during continuous speech recognition
EP1346343B1 (fr) Reconnaissance de la parole faisant appel a une instruction de mot dans une phrase
EP0965979B1 (fr) Manipulation de position dans la reconnaissance de la parole
US4829576A (en) Voice recognition system
US5027406A (en) Method for interactive speech recognition and training
EP0867857B1 (fr) Enrôlement dans la reconnaissance de la parole
US5995928A (en) Method and apparatus for continuous spelling speech recognition with early identification
US5899976A (en) Method and system for buffering recognized words during speech recognition
US7324939B1 (en) Phonetic searching
US6092044A (en) Pronunciation generation in speech recognition
EP1012827B1 (fr) Systeme de reconnaissance vocale servant a reconnaitre une expression vocale continue et isolee
US5787230A (en) System and method of intelligent Mandarin speech input for Chinese computers
EP0867858A2 (fr) Génération des prononciations dans la reconnaissance de la parole
US5987409A (en) Method of and apparatus for deriving a plurality of sequences of words from a speech signal
WO2002029615A1 (fr) Procede de recherche faisant appel a un arbre simple triphone utilise avec un logiciel de reconnaissance vocale en continu a vocabulaire etendu
EP0840287A2 (fr) Méthode et système pour sélectionner des mots reconnus lors d&#39;une correction de la parole reconnue
Bahl et al. Continuous parameter acoustic processing for recognition of a natural speech corpus
EP1135768B1 (fr) Mode de verification orthographique dans un systeme de reconnaissance vocale

Legal Events

Date Code Title Description
STCF Information on status: patent grant

Free format text: PATENTED CASE

FEPP Fee payment procedure

Free format text: PAT HLDR NO LONGER CLAIMS SMALL ENT STAT AS INDIV INVENTOR (ORIGINAL EVENT CODE: LSM1); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

FPAY Fee payment

Year of fee payment: 4

FEPP Fee payment procedure

Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

REMI Maintenance fee reminder mailed
AS Assignment

Owner name: L & H HOLDINGS USA, INC., MASSACHUSETTS

Free format text: MERGER;ASSIGNOR:DRAGON SYSTEMS, INC.;REEL/FRAME:013362/0732

Effective date: 20000607

Owner name: SCANSOFT, INC., MASSACHUSETTS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:L & H HOLDINGS USA, INC.;REEL/FRAME:013362/0739

Effective date: 20011212

FEPP Fee payment procedure

Free format text: PAYER NUMBER DE-ASSIGNED (ORIGINAL EVENT CODE: RMPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

FPAY Fee payment

Year of fee payment: 8

AS Assignment

Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS

Free format text: CHANGE OF NAME;ASSIGNOR:SCANSOFT, INC.;REEL/FRAME:016851/0772

Effective date: 20051017

AS Assignment

Owner name: USB AG, STAMFORD BRANCH,CONNECTICUT

Free format text: SECURITY AGREEMENT;ASSIGNOR:NUANCE COMMUNICATIONS, INC.;REEL/FRAME:017435/0199

Effective date: 20060331

Owner name: USB AG, STAMFORD BRANCH, CONNECTICUT

Free format text: SECURITY AGREEMENT;ASSIGNOR:NUANCE COMMUNICATIONS, INC.;REEL/FRAME:017435/0199

Effective date: 20060331

AS Assignment

Owner name: USB AG. STAMFORD BRANCH,CONNECTICUT

Free format text: SECURITY AGREEMENT;ASSIGNOR:NUANCE COMMUNICATIONS, INC.;REEL/FRAME:018160/0909

Effective date: 20060331

Owner name: USB AG. STAMFORD BRANCH, CONNECTICUT

Free format text: SECURITY AGREEMENT;ASSIGNOR:NUANCE COMMUNICATIONS, INC.;REEL/FRAME:018160/0909

Effective date: 20060331

FEPP Fee payment procedure

Free format text: PAYER NUMBER DE-ASSIGNED (ORIGINAL EVENT CODE: RMPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

Free format text: PAYOR NUMBER ASSIGNED (ORIGINAL EVENT CODE: ASPN); ENTITY STATUS OF PATENT OWNER: LARGE ENTITY

FPAY Fee payment

Year of fee payment: 12

AS Assignment

Owner name: DICTAPHONE CORPORATION, A DELAWARE CORPORATION, AS

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: DICTAPHONE CORPORATION, A DELAWARE CORPORATION, AS

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: SPEECHWORKS INTERNATIONAL, INC., A DELAWARE CORPOR

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: SPEECHWORKS INTERNATIONAL, INC., A DELAWARE CORPOR

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: DSP, INC., D/B/A DIAMOND EQUIPMENT, A MAINE CORPOR

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: NUANCE COMMUNICATIONS, INC., AS GRANTOR, MASSACHUS

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: NUANCE COMMUNICATIONS, INC., AS GRANTOR, MASSACHUS

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: NOKIA CORPORATION, AS GRANTOR, FINLAND

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: SCANSOFT, INC., A DELAWARE CORPORATION, AS GRANTOR

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: SCANSOFT, INC., A DELAWARE CORPORATION, AS GRANTOR

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: DSP, INC., D/B/A DIAMOND EQUIPMENT, A MAINE CORPOR

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: TELELOGUE, INC., A DELAWARE CORPORATION, AS GRANTO

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: HUMAN CAPITAL RESOURCES, INC., A DELAWARE CORPORAT

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DEL

Free format text: PATENT RELEASE (REEL:017435/FRAME:0199);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0824

Effective date: 20160520

Owner name: MITSUBISH DENKI KABUSHIKI KAISHA, AS GRANTOR, JAPA

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: STRYKER LEIBINGER GMBH & CO., KG, AS GRANTOR, GERM

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: INSTITIT KATALIZA IMENI G.K. BORESKOVA SIBIRSKOGO

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: NORTHROP GRUMMAN CORPORATION, A DELAWARE CORPORATI

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: ART ADVANCED RECOGNITION TECHNOLOGIES, INC., A DEL

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520

Owner name: TELELOGUE, INC., A DELAWARE CORPORATION, AS GRANTO

Free format text: PATENT RELEASE (REEL:018160/FRAME:0909);ASSIGNOR:MORGAN STANLEY SENIOR FUNDING, INC., AS ADMINISTRATIVE AGENT;REEL/FRAME:038770/0869

Effective date: 20160520